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检索条件"主题词=Backpropagation Algorithms"

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Unsupervised information theory-based training algorithms for multilayer neural networks

Unsupervised information theory-based training algorithms fo...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： G. Rigoll Furui Research Laboratory NTT Human Interface Laboratories Musashino Tokyo Japan

The author describes a novel learning algorithm for multilayer neural networks. The training neural networks are used as a vector quantizer (VQ) in a hidden Markov model (HMM)-based speech recognition system. The approach offers the following innovations: (1) it represents an unsupervised learning algorithm for multilayer neural networks. (Usually, multilayer neural networks are only trained in supervised mode); (2) information theory principles are used as learning criteria for the neural networks; and (3) the neural networks are not trained using the standard backpropagation algorithm, by using instead a new unsupervised learning procedure. The use of a neural network as a VQ trained with the new algorithm in combination with an HMM-based speech recognition system results in a 25% error reduction compared to the same HMM system using a standard k-means vector quantizer.< >

关键词： Multi-layer neural network Neural networks Hidden Markov models Speech recognition Information theory Neurons backpropagation algorithms Laboratories Unsupervised learning Humans

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Unsupervised global optimization: applications on classification of handwritten digits and visual evoked potentials

Unsupervised global optimization: applications on classifica...

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IEEE International Conference on Systems, Man and Cybernetics

作者： E. Micheli-Tzanakou T.J. Dasey Department of Biomedical Engineering Rutgers University Piscataway NJ USA

The authors discuss the optical recognition of handwritten unconnected numerals and visual evoked potential (VEP) classification using two neural network learning paradigms. The first is an unsupervised approach, trained by the combinatorial optimization routine ALOPEX, while the second method uses the backpropagation algorithm. The unsupervised ALOPEX trained system classifies 1000 training digits to an accuracy of 86.3%, and 500 generalizing characters 86.0% accurately. This compares to 99.8% and 93% for a network trained with the supervised backpropagation algorithm. The system was used to cluster the VEPs of normal and multiple sclerosis (MS) subjects. The method demonstrates two distinct groups of subjects, which when histogrammed illustrate that they largely correspond to the MS and control subject groups. A suitable threshold can be chosen so that the recognizer chooses no false negatives.< >

关键词： backpropagation algorithms Medical diagnostic imaging Neural networks Pattern recognition System testing Feature extraction Multiple sclerosis Character recognition Biomedical engineering Artificial neural networks

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Backward construction-a decomposed learning method for robot force/position control

Backward construction-a decomposed learning method for robot...

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International Joint Conference on Neural Networks (IJCNN)

作者： Hai-Long Pei T.P. Leung Qi-Jie Zhou Department of Automation South China University of Technology Guangzhou China Department of Mechanical and Marine Engineering Hong Kong Polytechnic Institute Hong Kong China

A decomposed learning method, where the motor task is partitioned into several stages by virtue of its task characters, is proposed. The controller can be implemented by neural networks which can be trained partially in different stages. Some of these neural networks, which can be called output networks, directly drive the objects to move along the desired trajectories. The others perform some virtual activations to feed the output networks to make them have a proper output; these are called internal networks. Simple samples that can describe the expected partial characters are engaged in each stage. The well-trained parts can be used to help to train the other parts. The approach uses the notion of backward construction to build backwards the interpretation of the desired motor strategy progressively. This approach is demonstrated in detail with an example of robotic manipulator position/force learning control.< >

关键词： Learning systems Force control Position control Robot control Manipulators Neural networks Robotics and automation Robot sensing systems Supervised learning backpropagation algorithms

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Neural networks as a tool to generate pattern classification algorithms

Neural networks as a tool to generate pattern classification...

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International Joint Conference on Neural Networks (IJCNN)

作者： S. Abe M. Kayama H. Takenaga T. Kitamura Hitachi Research Laboratory Hitachi and Limited Hitachi Japan

Extraction of algorithms from pattern classification neural networks is discussed. It is shown that input-to-hidden weights for three layers, or otherwise, hidden-to-hidden weights for four layers are determined so that corresponding hyperplanes separate all the training data in a class from the remaining classes. The extraction of a classification algorithm from a trained network is discussed, and its generalization ability is compared with that of the original network. Weights of the neural networks, generated by the backpropagation algorithm for number recognition, are tuned, and classification algorithms are demonstrated to be extracted.< >

关键词： Neural networks Pattern classification Classification algorithms Neurons Training data backpropagation algorithms Network synthesis Data mining Laboratories Multi-layer neural network

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A method of processing unknown attribute values by ID3

A method of processing unknown attribute values by ID3

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International Conference on Computing and Information (ICCI)

作者： P. Brazdil I. Bruha LIACC-CIUP Porto Portugal McMaster University Hamilton ONT Canada

The original inductive learning algorithm ID3 assumes that all attribute values of all examples are available both within the learning and classification process. Several techniques of processing of unknown attribute values by ID3 have been designed and implemented. The paper firstly briefly surveys these techniques, then discusses the authors' modified technique for unknown value processing and compares some of the techniques.< >

关键词： Decision trees backpropagation algorithms Process design Terminology Neural networks Partitioning algorithms

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Artificial neural network nuclear cardiac stress test interpretation

Artificial neural network nuclear cardiac stress test interp...

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IEEE Symposium on Nuclear Science (NSS/MIC)

作者： D.C. Wang J.E. Juni Henry Ford Hospital Detroit MI USA William Beaumont Hospital Royal Oak MI USA

A neural network can be used to provide a preliminary interpretation of nuclear cardiac stress tests, identifying areas of ischemia and infarction. A three-layer neural network was trained with bulls-eye plot images of simulated data and actual patient data using a standard backpropagation algorithm. Testing was then performed using 100 male patients who had undergone cardiac catheterization within two months of their stress thallium study. Using cardiac catheterization as the gold standard, the sensitivity and positive predictive values were calculated for studies read by a trained physician utilizing the tomographic images as well as bulls-eye plots, and for studies read by the neural network utilizing the bulls-eye plots alone. The neural network's sensitivity (84%) approached that of the experienced physician (95%) and the positive predictive value (90%) was similar to that of the physician (91%).< >

关键词： Artificial neural networks Stress Testing Catheterization Neural networks Ischemic pain backpropagation algorithms Performance evaluation Gold Tomography

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Learning state space trajectories in cellular neural networks

Learning state space trajectories in cellular neural network...

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IEEE International Workshop on Cellular Nanoscale Networks and their Applications, (CNNA)

作者： A.J. Schuler P. Nachbar J.A. Nossek L.O. Chua Institute for Network Theory and Circuit Design Technical University Munich Germany Electronics Research Laboratory College of Engineering University of California USA

A learning algorithm similar to the backpropagation-through-time approach is presented. The algorithm is based on the minimization of an error criterion, which is defined as the product of a function of the state at a given time and the integral of an entire time function of the state over the trajectory prior to this time. The technique of the calculus-of-variation is used to evaluate the gradient of the error in the parameter space, which can be used to descend to a minimum on the error surface. This theory is adapted to cellular network networks (CNNs) and some simple examples of the learning of CNN parameters are shown.< >

关键词： State-space methods Intelligent networks Cellular neural networks backpropagation algorithms Differential equations Neural networks Time measurement Circuit synthesis Laboratories Educational institutions

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Model reference adaptive control with recurrent networks trained by the dynamic DEKF algorithm

Model reference adaptive control with recurrent networks tra...

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International Joint Conference on Neural Networks (IJCNN)

作者： G.V. Puskorius L.A. Feldkamp Ford Motor Company Limited Dearborn MI USA

Two fundamental extensions of the dynamic backpropagation (DBP) gradient descent procedure which generally result in faster convergence times and higher quality solutions are presented. The decoupled extended Kalman filter training algorithm (DEKF) for feedforward layered networks is extended to the training of neural controllers in a dynamic indirect adaptive control scheme; the resulting algorithm is called dynamic DEKF (or DDEKF). The DDEKF neural controller training algorithm is extended to include control network architectures with explicit internal feedback connections. It is demonstrated that the DDEKF algorithm has computational complexity and requirements that are similar to those of DBP for control networks with a large number of recurrent connections. The use of these extensions for a model reference adaptive control (MRAC) problem in which the example dynamical system is highly nonlinear and does not possess a unique inverse is presented.< >

关键词： Adaptive control Heuristic algorithms backpropagation algorithms Control systems Computer networks Algorithm design and analysis Neural networks Guidelines Acceleration Design methodology

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A novel multilayer neural networks training algorithm that minimizes the probability of classification error

A novel multilayer neural networks training algorithm that m...

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International Conference on Pattern Recognition

作者： V. Nedeljkovic Faculty of Electrical Engineering University of Belgrade Belgrade Serbia

A new multilayer neural network training algorithm that minimizes the probability of classification error is proposed. The claim is made that such an algorithm possesses some clear advantages over the standard backpropagation (BP) algorithm. The convergence analysis of the proposed procedure is performed and a set of sufficient conditions which insure the convergence of the criterion sequence with probability one is given. An experimental comparison with the BP algorithm on two artificial pattern recognition problems is also given.< >

关键词： Multi-layer neural network Neural networks Stochastic processes backpropagation algorithms Convergence Pattern recognition Artificial neural networks Feedforward neural networks Sufficient conditions Bayesian methods

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Diagonal recurrent neural network based control using adaptive learning rates

Diagonal recurrent neural network based control using adapti...

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IEEE Conference on Decision and Control

作者： C.-C. Ku K.Y. Lee Department of Electrical and Computer Engineering Pennsylvania State University University Park PA USA

A method of choosing learning rates adaptively in controlling a dynamic system using diagonal recurrent neural networks is presented. The architecture of DRNN is a modified model of the fully connected recurrent neural network with one hidden layer. The hidden layer is comprised of self-recurrent neurons, each feeding its output only into itself and not to other neurons in the hidden layer. An unknown plant is identified by a diagonal recurrent neuroidentifier (DRNI), and provides the sensitivity information of the plant to a diagonal recurrent neurocontroller (DRNC). A dynamic backpropagation algorithm (DBP) is used to train both DRNC and DRNI. The DRNN captures the dynamic nature of a system, and, since it is not fully connected, training is much faster than for the case of a fully connected recurrent neural network. For faster learning, the use of an adaptive learning rate that guarantees convergence is developed. The proposed approach is applied to numerical problems. Simulation results are included.< >

关键词： Recurrent neural networks Programmable control Adaptive control Neurons Control systems Neurocontrollers backpropagation algorithms Convergence

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